Meta AR/VR Job | Research Scientist Physics Simulation
Citys（岗位城市）: Redmond, WA
We are currently seeking innovative and self-motivated scientists to advance anthropometric physical-simulation for designing novel physics-models to push the limits of digital-twinning of complex real-world systems pertaining to wearable devices. An ideal candidate would come with an advanced knowledge in physics-simulation, computational mechanics, and some exposure to machine-learning to take a real-world system and model it using it’s underlying physics or physics-informed learning problems, collaborate with cross-functional teams to collect ground-truth and validate the model against the ground-truth. The candidate should excel at working in a dynamic cross-functional environment with great communication skills.
Currently has, or is in the process of obtaining a Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
Ph.D. degree in the field of Physics, Computational Physics/Mechanics/Aerospace, or a similar field.
3+ years of experience, working/researching in any combinations of finite element modeling and analysis, computational mechanics, computational physics, advanced optimization techniques, machine learning.
Proficient with Python and numerical analysis packages and commercial softwares for simulation such as COMSOL, Ansys etc.
Make fundamental progress to the current physics-simulations towards anthropometric focused design optimization of AR/VR devices and validate the results through real-world experiments.
Develop novel algorithms, either through reduced-order modeling or leveraging a learning-framework, to deploy real-time physics-simulations on compute/memory restricted platforms.
Develop systems for physics-predictions using a simulation/learning framework and drive the required data-collection, modeling, development, validation and deployment with cross-functional collaborations.
Engage the wider academic community to pursue research in this direction through publications and/or workshop/challenge/tutorial organization top-tier conferences.
Ph.D. degree in Computational Science, Machine Learning, Computer Science.
Experience with bio-mechanical and custom physics modeling & simulations.
Leadership experience of managing cross-collaborative projects in simulation and their deployment.
Experience working on novel physics-simulation and machine-learning problems exhibited in the form of publications in top-tier conferences/journals such as ICLR, NeurIPS, ICML and similar venues.